Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with critical, precise reasoning capabilities akin to those of top human experts. Existing AI systems often excel at predefined tasks but struggle with adaptability and precision in novel problem-solving. To overcome this, AEI introduces a framework for ``Probably Approximately Correct (PAC) Reasoning". This paradigm provides robust theoretical guarantees for reliably decomposing complex problems, with a practical mechanism for controlling reasoning precision. In reference to the division of human thought into System 1 for intuitive thinking and System 2 for reflective reasoning~\citep{tversky1974judgment}, we refer to this new type of reasoning as System 3 for precise reasoning, inspired by the rigor of the scientific method. AEI thus establishes a foundation for error-bounded, inference-time learning.

翻译：人工专家智能（AEI）旨在超越通用人工智能（AGI）与狭义人工智能的局限，其核心在于将领域专业知识与顶尖人类专家所具备的批判性、精确推理能力相结合。现有的人工智能系统通常在预定义任务上表现出色，但在面对新问题求解时的适应性与精确性方面存在不足。为克服这一局限，AEI引入了“概率近似正确（PAC）推理”框架。该范式为可靠地分解复杂问题提供了坚实的理论保证，并具备控制推理精度的实用机制。借鉴人类思维被划分为负责直觉思考的系统1与负责反思性推理的系统2（Tversky & Kahneman, 1974），我们将这种新型推理机制称为系统3——精确推理系统，其设计灵感源于科学方法的严谨性。由此，AEI为具有误差界限的推理时学习奠定了理论基础。